Introduction
Properties
Large aspect ratio(>1000).
Atomically Sharp tips.
High temperature and chemical stability.
High electrical and thermal conductivity.
Can be either Metal or semiconductor.
Problems in the Field
Experimental data doesn’t match the theoretical
expectations due to quality issues.
Comprehensive theoretical models are still an
approximation, which doesn’t involve the
distinctiveness of every sample.
Methods for deterministic (controlled) synthesis
and assembly of carbon nanostructures for novel
devices and materials have not been developed.
“Nanotube suppliers accused of selling shoddy
goods” by Jim Giles “ Researchers who buy products
such as carbon nanotubes are frequently being sold
defective materials, according to a survey of
nanotechnology companies. ” [news@nature published
online: 10 December 2004 | doi:10.1038/news041206-15.]
Raman SpectroscopyNondestructive, lots of information content
(wavelength, polarization, batch average over micron
scale). Research has proven that some geometrical
information are embedded in Raman Spectrum [2].
Image Processing
2D-FFT (Fast Fourier Transform) analysis.
2D Stereological Relations:
Relative Alignment measurements:
Measured based on the variance of the relative distances di, between even
segments, si, of any two nanotubes.
Where N is the total number of segments, and M is the number of two
nanotube combinations.
Relative Curvature measurements:
The curvature, k, at considered segments is defined
as the rate of rotation, , of the nanotube tangent
as the contact point moves along the segment length, s:
Artificial Neural NetworksBackpropagation training methodology is used in
training the type of neural network [3].
Quality Analysis MethodologyRaman spectroscopy and image processing analysis are combined to represent
the sample geometrical characteristics. This empirical model will assist the
quality control process of CNT production, enabling manufacturers to achieve
desired properties by monitoring the difference between both desired CNT
analysis and the CNT under nucleation. The flow chart in fig.4 shows the main
blocks of the implemented procedure.
Technical ProcedureThe procedures for analyzing nanotubes are:
1)Filtering and thresholding the SEM image, and enhancing the signal-to-noise
ratio in Raman spectra using Wavelets de-nosing techniques.
2)Extracting the morphology of the nanostructure using image processing
techniques of Scanning Electron Microscope (SEM) images, stereological
relations and curvature and alignment measurements.
3)Raman spectroscopy investigation of the characterized CNT specimens is
performed using multiple excitation wavelengths.
4)The relationship between the experimental spectroscopic analysis and the
morphology of the structure will be modeled using Artificial Neural Networks
(ANN).
Experiment ProcedureSEM Imaging
Raman Spectroscopy
CharacterizationImage Processing
Fig1. CNTs structures
FFT Analysis
FFT was applied to both real images and ideal images, to understand the meaning
of the results based on CNTs alignment, curvature, orientation, and thickness
properties.
Stereological Analysis
Raman Spectrum
Filtering using Wavelet data processing [4]
Peaks info.
Empirical ModelingArtificial neural network model for CNTs quality classification
ConclusionModeling using Raman Spectroscopy analyses and
SEM image processing will link CNT properties with
growth conditions, which could be used later to
manipulate the nucleation factors such that the CNT
desired properties in large quantities will be
achieved.
Neural networks classifier is very helpful to
categorize the images based on alignment and purity
characteristics. Empirical modeling is a powerful tool
to predict, explain, and model materials behavior,
especially when the analytical modeling is not
achievable.
Fig3. Artificial Neural Network Architecture
1
1
1
,
1 k
i
k
j
s
j
k
ji
k
ibawn
k
)( 111 k
i
kk
infa
1))*2exp(1(
2)(
nnTansig
k
ji
k
ji
w
Vw
,
,
Quality Classification via Raman Identification of Carbon Nanotube Bundles Using Artificial Neural Networks
M.Al-Khedher¹, C. Pezeshki¹, J. McHale², F. Knorr²¹ School of Mechanical and Materials Engineering, ² Department of Chemistry
Washington State University, Pullman, WA 99164-2920, USA
Fig7. Auto-Threshold (a) raw image (b) correlation vs. Threshold (c) Thresholded image at max
correlation.
Fig.8 Different FFT patterns of created ideal images (a-c) and SEM images (d-e).
Fig10. (a) Raw portion of Raman signal (b) DWT and IDWT data processing (c) filtered Raman signal.
Fig12. Model outputs for (a) sample1 and (b) sample2 alignment estimations, (c) sample1 and (d) sample2
curvature estimations. Insets are the SEM images of the analyzed samples.
AbstractThe exceptional properties of Carbon Nanotubes
(CNTs) have attracted much attention in recent years
for their small dimensions, high electrical and thermal
conductivities and unique morphologies [1].
It is important to control the structure of these CNTs if
we want to control the properties they exhibit for
mass-production purposes. The methodology is
basically correlating the morphology of the structure,
which is extracted using image processing techniques
of Scanning Electron Microscope (SEM) images, and
Raman Spectroscopy analysis with the mechanical
properties of the structure using Artificial Neural
Network modeling technique. The model will play a
significant role in analyzing and predicting the CNTs’
properties, which will eventually help in designing the
desired CNT structure.
Fig5. SEM images of the turf (a) corner view (b) detail (c) SEM of different samples
• Area Fraction:
• Perimeter length per unit area:
• Line intercept count:
w w
CNTA
A
AA
probe
tionserL
L
PP secint
w w
boundaryCNT
AA
LL
_
Fig2. Raman Spectroscopy of SWNT & MWNT
Raman Spectroscopy
of CNTs
Quantitative Analysis
of Raman Spectrum
SEM Images of CNTs
turfs (bulk material)
Image Analysis for
Numerical Representation
of Images
Artificial
Neural Network
Model for CNTs
Quality
Classification
Artificial
Neural Network
for
Raman Spectrum
Identification
Fig.4 Scope of the project.
References
[1] P Harris, Carbon Nanotubes and Related Structures,
Cambridge University Press , 1999.
[2] Rao, A. M., Richter, E., Bandow, S., Chase, B.,
Eklund, P. C., Williams, K. A., Fang, S., Subbaswamy,
K. R., Menon, M., Thess, A., Smalley, R. E.,
Dresselhaus, G., and Dresselhaus, M. S. Diameter-
selective raman scattering from vibrational modes in
carbon nanotubes, Science 275, pages 187-191 1997.
[3] K K Tho, S Swaddiwudhipong, Z S Liu and J Hua,
Artificial neural network model for material
characterization by indentation, Modelling and
Simulation in Materials Science and Engineering Vol.
12 Issue 5 Article 019 2004-09-01.
[4] C Camerlingo, F Zenone, G M Gaeta, R Riccio and
M Lepore,C Camerlingo et al Wavelet data processing
of micro-Raman spectra of biological samples, Meas.
Sci. Technol. 17 298-303 2006.
Fig6. Raman Spectra of two different CNT samples taken at two excitation
wavelengths: 488.0 and 530.9 nm. The shown range is: 600cm-1 -3400cm-1.
M
1j
N
1i
2i
M
1j
i ))dd(N
1(
M
1))d(var(
M
1A
ds
dk
IDWTDWT
2500 2600 2700 2800 2900 3000 310020
30
40
50
60
70
80
90
100
Relative wavenumber (cm-1)
Rela
tive I
nte
nsity(c
ounts
)
2500 2650 2800 2950 3100
D8
D7
D6
D5
D4
D3
D2
D1
(b)
(c)(a)
Fig11. Lorentzian fitting of 2D band at 2718cm-1.
0 50 100 150 200 250 3000
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Threshold
corr
ela
tion
image after thresholding at the peak correlation
500 1000 1500 2000 2500
200
400
600
800
1000
1200
1400
1600
1800
(a) (b) (c)
Discussion• Understanding the correlation between CNT growth
aspects and its properties is the key factor to develop
quality control methods for controlled synthesis and
assembly of carbon nanotubes.
• The system is built to model and estimate CNTs
quality based on Raman Spectroscopy, morphology
information extracted from SEM images using in-
house image processing analysis.
• FFT statistics were a good indicator for CNTs
alignment, curvature, orientation, and thickness
properties.
• Wavelet de-noising analysis produced a smoothed
Raman signal without loosing any peaks information
embedded in the spectrum.
• Artificial neural network model for CNTs alignment
classification using image processing results shows an
accurate categorizing technique for CNTs with an
acceptable range of approximation.
• ANN was able to model the Raman data and extract
the influence of the given inputs. The effect of the
excitation wavelength and CNT morphology was
intensely studied.
(a) (b) (c)
(a1)
(a2)
(b1)
(b2)
(c1)
(c2)
(d1)
(d2)
Fig.9 (a) SEM image samples of analyzed specimen for the measurements of: (b) AA (C) LA (d) PL( ).
Quality evaluation using Raman Spectra
(a) (b)Fig.13 Effect of excitation wavelength on (a) D, (b) 2D peak positions.
Fig.14 ANN results for the effect of (a) LA (b) PL on the HWHM of G line.(a) (b)
(e)(d)
image after thresholding at the peak correlation
image after thresholding at the peak correlation
Fig.15 Neural network results of the D line (a) Upshift in D band as a
function of PL , (b) Upshift in 2D band as a function of LA
(a) (b)